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Data Profile
 Data profile: cancer sample cohorts (stomach, breast, colorectal, and liver) in Korea
Daewoo Pak1orcid, Suk Yong Jang2,3orcid, Jin-Ha Yoon4,5,6orcid, Dong Wook Kim7,8orcid, Jin-Won Noh9,10orcid, Dong-Woo Choi11orcid, Minyeong Guk11, Hyeri Kim11, Ju-Won Oh11, Heejung Chae11,12orcid, Hyun-Joo Kong11, Gi Hyun Kim13orcid, Ji Woong Nam14orcid, Ga Ram Lee15orcid, Dayun Park16, Jehoo Jeon17, Byungyoon Yun4,5,6orcid, Ki-Bong Yoo9,10orcid, Kui Son Choi11,18orcid
Epidemiol Health 2025;47:e2025058.
DOI: https://doi.org/10.4178/epih.e2025058
Published online: October 14, 2025

1Division of Data Science, Yonsei University Mirae Campus, Wonju, Korea

2Institute of Health Services Research, Yonsei University, Seoul, Korea

3Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul, Korea

4The Institute for Occupational Health, Yonsei University College of Medicine, Seoul, Korea

5Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea

6Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, Korea

7Department of Information and Statistics, Gyeongsang National University, Jinju, Korea

8Department of Bio & Medical Big Data, Research Institute of Natural Science, Gyeongsang National University, Jinju, Korea

9Division of Health Administration, Yonsei University Mirae Campus, Wonju, Korea

10Institute for Planetary Health, Yonsei University, Wonju, Korea

11Cancer Data Center, National Cancer Control Institute, National Cancer Center, Goyang, Korea

12Center for Breast Cancer, Hospital, National Cancer Center, Goyang, Korea

13Health Insurance Research Institute, National Health Insurance Service, Wonju, Korea

14Department of Health Administration, Yonsei University Mirae Campus, Wonju, Korea

15Department of Eligibility and Imposition Eastern Part Cheongju, National Health Insurance Service, Cheongju, Korea

16Department of Image Processing Algorithm Development, Imaging R&D Center, Osstem implant Co., Ltd., Seoul, Korea

17AI Part, Planning AI Division, New Power Plasma Co., Ltd., Suwon, Korea

18Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea

Correspondence: Ki-Bong Yoo Division of Health Administration, Yonsei University Mirae Campus, 1 Yeonsedae-gil, Wonju 26493, Korea E-mail: ykbong@yonsei.ac.kr
Co-correspondence: Kui Son Choi Graduate School of Cancer Science and Policy, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea E-mail: kschoi@ncc.re.kr
• Received: May 8, 2025   • Accepted: September 9, 2025

© 2025, Korean Society of Epidemiology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Cancer Public Library Database (CPLD) links data from four major population-based public sources: the Korea National Cancer Incidence Database in the Korea Central Cancer Registry, cause-of-death data in Statistics Korea, the National Health Information Database in the National Health Insurance Service, and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service. The National Cancer Data Center has developed a new nationally representative sample cohort dataset from Korean Clinical Data Utilization for Research Excellence project (K-CURE) CPLD: Stomach Cancer Sample Cohort, Breast Cancer Sample Cohort, Colorectal Cancer Sample Cohort, and Liver Cancer Sample Cohort. The sample populations consisted of approximately 21% of all cancer patients from 2012 to 2019. The populations of the Stomach Cancer Sample Cohort, Breast Cancer Sample Cohort, Colorectal Cancer Sample Cohort, and Liver Cancer Sample Cohort were 51,951, 39,586, 53,485, and 27,375 patients, respectively. The dataset included cancer incidence information, demographics, socioeconomic variables, health utilization data (procedures, diagnoses, and medications), general health checkup data, cancer screening data before and after the cancer incidence, as well as death information. These cohorts could help researchers analyze time-to-event data on mortality, treatment outcomes, comorbid conditions following a cancer diagnosis, and cancer incidence risk factors. The data can be accessed through the K-CURE portal (https://k-cure.mohw.go.kr/).
  • Keywords: Data profile, Breast neoplasms, Stomach neoplasms, Liver neoplasms, Colorectal neoplasms
The need for samples and customized databases is emerging in Korea with the increasing demand for public health big data [1-3]. The frequently used national health insurance big data includes the entire population, making it an excellent data source for representativeness and sample size [4]. In Korea, this data source comprises three institutions: the Health Insurance Review & Assessment Service (HIRA), the National Health Insurance Service (NHIS), and Statistics Korea. HIRA encompasses health utilization, including expenditures, length of stay, encounter types, procedure codes, diagnosis information, and medications. NHIS manages insurance eligibility and health examination data, while Statistics Korea comprises cause-of-death data.
The HIRA and NHIS have released various sample cohort data [4-8]. However, these datasets include health utilization information based on only claims data. They lack detailed clinical information and laboratory tests and have limitations regarding coding accuracy [9]. Additionally, these data sources cannot identify the cancer stage and detailed topography. In contrast, the Korea Central Cancer Registry (KCCR) managed by the National Cancer Center (NCC) consists of staging and detailed clinical information on cancer, offering reliable data accuracy [10]. However, it lacks data on the socioeconomic status and healthcare utilization in patients with cancer [11-13].
Consequently, combining these databases can complement their strengths and weaknesses. The National Cancer Data Center (NCDC) of the NCC developed a novel database called the Cancer Public Library Database (CPLD) under the Korean Clinical Data Utilization for Research Excellence project (K-CURE) [14]. The NCDC began providing CPLD data for research purposes on demand in 2023. The customized data based on demand can satisfy various research topics; however, its use is restricted due to de-identification issues and security concerns. Therefore, researchers are required to visit designated offline research centers to access the data. To address this inconvenience, the NCDC has developed and opened sample cohorts for stomach, breast, colorectal, and liver cancers to promote cancer research with a simple and fast administration process and a remotely accessible environment.
All cancer sample cohorts were derived from the CPLD, which links data from the KCCR, HIRA health insurance claim data, NHIS insurance eligibility and examination data, and cause-of-death data of Statistics Korea (Figure 1). As a cancer registry data, KCCR has included all patients diagnosed with cancer or treated in hospitals since 1999 [15]. To merge data from multiple institutes, linkage tables for the Substitute Identification Key were generated for internal integration and de-identification across the four institutions. The Substitute Identification Key is a series of values assigned by the data-providing institutions to uniquely identify individuals. Per the Personal Information Protection Act and the Cancer Control Act [16], the data-providing institutions share de-identified personal information after anonymization upon requesting data from the NCDC.
We identified the target population to construct nationally representative cohorts as follows. Among patients with stomach cancer, breast cancer, colorectal cancer, and liver cancer in KCCR data, we excluded secondary cancers and missing data for the age, sex, region, year of diagnosis, cancer type, and cancer stage. To protect privacy, if there were fewer than five individuals within a strata defined by cancer diagnosis year, sex, age at diagnosis, region (metropolitan, city, or rural), cancer type, or cancer stage, such strata were excluded from the population. The target population of patients with breast cancer exclusively comprised females. So the target population of all cancer patients registered between 2012 and 2019 included 248,103 patients with stomach cancer, 192,907 with breast cancer, 261,291 with colorectal cancer, and 124,324 with liver cancer.
Stratified random sampling with proportional allocation was used to construct a representative sample cohort for each cancer type from the target population using. Sampling is conducted to extract newly registered patients with cancer every year. The population units were partitioned into strata defined by the year of diagnosis, sex, age at diagnosis, region, and Surveillance, Epidemiology, and End Results (SEER) summary stage (in situ, localized, regional, distant, and unknown). Additionally, a probability sample of units was selected from each stratum, while ensuring the representativeness of the total annual medical costs within the stratum. Owing to the small strata for patients aged <39 years or >80 years diagnosed with cancer, the age at diagnosis for stratification was recategorized into six groups as follows: <39 years, 10-year intervals between 40 years and 79 years, and ≥80 years. The region was categorized into 17 groups, namely, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, Sejong, Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju, for sampling stratification. However, it was recategorized into three groups: metropolitan, city, and rural, for public sample data because of de-identification. We constructed 3,117 strata for breast cancer, 6,021 for stomach cancer, 5,218 for liver cancer, and 6,296 for colorectal cancer.
In each stratum, samples were drawn at a sampling rate of 20%, much larger than the theoretically derived sizes required to achieve a 1% margin of error for the mean of total annual medical expenses. Due to de-identification, the sampling rates were limited to approximately 20%. Their representativeness was assessed by determining whether the population averages of the stratum fell within a 95% confidence interval (CI) for sampling without replacement, which is defined as
CI=y¯h±1.96Nh−nhNh−1σhnh,
where, for the h-th stratum, y¯h is the sample mean, Nh and nh are the population and sample sizes, respectively, and σh is the known population standard deviation. If the representativeness was not achieved for a stratum, independent random sampling was conducted up to 50 times to generate candidate sets of samples for the stratum. Among those ensuring representativeness, the samples with the mean closest to the population mean of the stratum were selected as the final samples. If the initial sample size was insufficient to achieve representativeness, we incrementally increased the sample size for the stratum and repeated the process until the representativeness was attained.
Finally, the samples of the stomach, breast, colorectal, and liver cancer sample cohorts comprised 51,951 (20.9%), 39,586 (20.5%), 53,485 (20.5%), and 27,375 (22.0%) patients, respectively (Table 1). The age-specific distribution of cancer incidence varied across cancer types. Among patients aged 60 years or older, the proportions were 65.2% for stomach cancer, 25.0% for breast cancer, 66.2% for colorectal cancer, and 61.8% for liver cancer, and these age-related incidence patterns were well represented in the sample cohorts. Note that the incidence of stomach, colorectal, and liver cancers increases with age due to the cumulative effects of carcinogenic exposures and biological aging [17], while breast cancer accounts for a lower proportion in older age groups because it more commonly occurs in their 40s and 50s in Asian populations, including Korea [18]. All cancers, except breast cancer, for which the population consisted only of female, showed a higher incidence in male than in female, with male-to-female rate ratios of 2.1 for stomach cancer, 1.5 for colorectal cancer, and 3.0 for liver cancer with corresponding values of 2.0, 1.5, and 2.6 in the sample cohorts, respectively. There were no substantial differences in the distribution of cancer incidence by year of diagnosis across all cancer types during the study period. The variables from the public database were attached to selected patients from 2012 to 2021 (Figure 2). The NCC plans to update the cancer clinical library sample database annually.
The representativeness of the follow-up years cannot be guaranteed; nonetheless, a sufficient sample size for the initial cohort could help ensure representativeness. We assessed and confirmed the representativeness of all strata by examining whether the 95% CI for sampling without replacement consisted of the average total annual medical expenses and statistical power. Table 2 summarizes the frequencies and medical costs by cancer type and age group. Other stratification variables were omitted in Table 2. The medical costs in sample cohorts by cancer type and age group accounted for the medical costs of the population. Furthermore, it shows a clear trend of increasing medical costs with advancing age across most cancer types, particularly for stomach and colorectal cancers. Liver cancer exhibited the highest average medical costs across all age groups, while stomach cancer showed relatively lower medical costs compared to other cancer types. Breast cancer had the highest costs on average in the 50-69 age groups. The similarity in average medical costs between the sample cohorts and the population dataset further demonstrates the representativeness of the sample cohorts. These patterns highlight the age-related economic burden of cancer care and underscore the potential utility of this dataset for studies on healthcare resource allocation.
In Figure 3, Kaplan–Meier survival curves for patients in both the population and sample cohorts are depicted by sex for each cancer type. Both the population and sample cohorts showed similar survival curves. In each stratum, we further evaluated whether the samples adequately represented the survival distribution of the population using a one-sample log-rank test. This test is commonly used to compare the survival curve of a sample with that of a defined reference population [19]. The proportion of strata where the test did not reject the null hypothesis—that the survival of the sample population is the same as that of the corresponding population—at a significance level of 0.05 was 91.1% for stomach cancer, 97.9% for breast cancer in females, 92.7% for colorectal cancer, and 93.8% for liver cancer.
The follow-up times for each cancer type were consistent between the population and the sample cohorts. The median follow-up times for the population (the sample cohort) were 3.5 (3.6) years for breast cancer, 4.0 (4.0) years for colorectal cancer, 4.3 (4.3) years for liver cancer, and 4.1 (4.1) years for stomach cancer. Furthermore, the maximum follow-up times for the population (the sample cohort) were 8.9 (8.8) years for breast cancer, 9.0 (8.9) years for colorectal cancer, 9.0 (8.8) years for liver cancer, and 8.9 (8.7) years for stomach cancer, while the minimum follow-up time was 0 years across all cancer types. We further examined the median follow-up times by age at diagnosis. For cases diagnosed in 2012, the median follow-up time was approximately 7.5 years across all cancer types in both the population and the sample cohorts. In contrast, for cases diagnosed in 2019, the median follow-up times were approximately 0.5 years for breast and stomach cancers, 0.6 years for colorectal cancer, and 0.7 years for liver cancer, with consistent patterns observed in both the population and the sample cohorts.
Almost the same database was used for all cohorts (Table 3). The information was as follows: KCCR data and cancer screening records (stomach, breast, colorectal, and liver cancers) from the KCCR from 2012 to 2019; health insurance claim data, such as health utilization data, diagnosis records, and prescription details from HIRA from 2012 to 2022; insurance eligibility data and general health checkups from NHIS from 2012 to 2022; and cause-of-death data from Statistics Korea from 2012 to 2021.
Stomach cancer screening included results of an esophagogastroduodenoscopy. Breast cancer screening included results of mammography and tissue biopsy. Liver cancer screening included abdominal ultrasonography and serum alpha-fetoprotein test; colorectal cancer screening included fecal occult blood test for the first test, and colonoscopy (biopsy) or double contrast barium enema for the second test. The K-CURE portal (https://k-cure.mohw.go.kr/) contains all information on the variables [14,20].
In summary, the sample cohort included detailed information on cancer incidence. Additionally, it included demographic and socioeconomic variables, health utilization data (including procedures, diagnoses, and medications), general health checkup data before and after cancer incidence, and death and cancer screening data. More variables in CPLD were present, but those related to administrative purposes were excluded from the sample cohorts.
Ethics statement
This sampling process was exempt from the Institutional Review Board of Yonsei University Mirae.
Several studies on machine learning research take a long time to analyze because the sample data is accessible remotely 24 hours a day. In Kang et al. [21]’s paper, a machine learning model was proposed to predict survival of young breast cancer patients. Cancer type was breast cancer defined as International Classification of Diseases, 10th revision (ICD-10) C500-C506, C508, and C509 and primary endpoint was all-cause mortality within 5 years of diagnosis. They concluded random survival forest, gradient boosting survival analysis, extra survival trees, and penalized Cox probability hazard lasso using Tensorflow version 1.15.5 and Python version 3.7.5. The highest area under the curve (AUC) were 0.87 in young patients and 0.83 in elderly patients by the extra survival trees model.
Kang et al. [22] published a study on mortality and gastric cancer. This research used various machine learning including extensions of gradient boosting machine models and random forests. Gastric cancer was defined as C16 in ICD-10 and the outcomes were the all-cause mortality and disease-specific (gastric cancer) mortality. The highest AUCs were 0.795 for all-cause mortality using the gradient boosting machine and 0.867 for disease-specific mortality using the light gradient boosting machine. This study used R version 3.8.1 (R Foundation for Statistical Computing, Vienna, Austria), Python version 3.8.10, Optuna version 3.3.0.
These two recent studies [21,22] demonstrate that researchers can conduct diverse prognostic analyses on cancer incidence cases by leveraging detailed cancer-related data and advanced analytical environments. While the NHIS National Sample Cohort [6] poses challenges for implementing machine learning methods due to computational limitations, NCDC facilitates such analyses by providing a Python-compatible environment through a remote virtual machine. It is expected to run not only prediction models, but also various causal inference models to find out the relationship between treatment, demographic or lab test variables and subsequent diseases or mortality.
Compared to Korean health insurance claims [9,23], the sample cohorts are specialized for cancer research. These datasets have detailed information on cancer and its screening [24,25]. Additionally, a sample representing approximately 21% of the data was selected to ensure representativeness. As the data are longitudinal, researchers can track all medical utilization and prognosis before and after cancer onset, as well as analyze the time-to-event for mortality, treatment outcomes, comorbidity conditions after cancer diagnosis, and risk factors of cancer incidence. In Korea, all medical services can be tracked through health insurance claim data because of a single national health insurance system. Moreover, as the sample cohorts are pre-established, the data usage application process is simplified, allowing researchers to access and analyze the data remotely.
This study has few limitations. The database does not include the general population without cancer as a control group; however, it will be updated in the future. Although approximately 20% of the sample satisfied the statistical representativeness, the absolute sample sizes were small. As the sample cohorts allow remote access, the higher the sampling percentage, the greater the possibility of personal identification. Hence, the number of samples cannot be increased to maintain security. However, compared to that in the existing cancer-related cohort data [26], the number of participants in our cancer sample cohorts is not small [27-29]. Therefore, our cancer sample cohorts are suitable for conducting general cancer research. The sample size might limit data analysis when considering secondary cancers, detailed comorbidities, or rare populations. In such cases, researchers can use customized data from CPLD. Data on uninsured treatments and medications, cosmetic procedures, laboratory tests, clinical information, and the use of over-the-counter medications were excluded.
The concept of the CPLD is similar to that of the National Cancer Institute’s SEER-Medicare–linked database [30]. Compared to the SEER-Medicare cohort, our cohorts lacked hospital information and a patient assessment variable; however, they included not only patients aged >65 years, but also younger patients because of the single-payer system. Korea has only one national health insurance system, which enables comprehensive tracking of each patient’s diagnosis, procedures, and examination history. Consequently, our cohort covered all age groups and health utilization before and after cancer diagnosis [31]. Therefore, our data are representative of the entire population.
In conclusion, the Stomach Cancer Sample Cohort, Breast Cancer Sample Cohort, Colorectal Cancer Sample Cohort, and Liver Cancer Sample Cohort are powerful data sources, providing information on health utilization, demographics, socioeconomic status, detailed cancer information, health checkups, and cancer screening results before and after cancer incidence. These cohorts will facilitate evidence for cancer prevention, diagnosis, treatment, and management. The K-CURE project plans to continuously open sample cohorts for different types of cancer. In 2025, it plans to open sample cohorts for lung and pancreatic cancer, followed by sample cohorts for kidney, cervical, and blood cancers in 2026.
The data can be accessed through the K-CURE portal (https://k-cure.mohw.go.kr/). A person must register on the portal and apply for access to the Cancer Sample Cohorts through the “Application for the Data” menu. Researchers must complete the application forms, research proposals, and institutional review board approval documents. Applications are reviewed by the Review Committee of the NCDC. Subsequently, the data and receipts are provided to the applicant at a fee. Sample cohorts can be accessed remotely through a virtual computer environment. Downloading data offline is impossible.

Conflict of interest

The authors have no conflicts of interest to declare for this study.

Funding

This work was supported by the National Cancer Center Grant (NCC-2310520-3).

Acknowledgements

None.

Author contributions

Conceptualization: Choi DW, Choi KS. Data curation: Pak D, Kim GH, Nam JW, Lee GR, Park D, Yoo KB. Formal analysis: Pak D, Yoon JH, Kim DW, Noh JW, Choi DW, Guk M, Kim H, Oh JW, Chae H, Kong HJ, Jeon J, Yoo KB, Yun B. Funding acquisition: Choi DW, Choi KS. Methodology: Pak D, Jang SY, Kim DW, Yoo KB, Choi KS. Project administration: Choi DW, Kim GH, Nam JW. Visualization: Pak D. Writing – original draft: Pak D, Jang SY, Yoon JH, Kim DW, Noh JW, Choi DW, Nam JW, Lee GR, Park D, Jeon J, Yun B, Yoo KB. Writing – review & editing: Guk M, Kim H, Oh JW, Chae H, Kong HJ, Kim GH, Yoo KB, Choi KS.

Figure 1.
Data structure of Cancer Public Library Database and four sample cohorts.
epih-47-e2025058f1.jpg
Figure 2.
Process of constructing the sample cohort data. NHIS, National Health Insurance Service; HIRA, Health Insurance Review & Assesment Service.
epih-47-e2025058f2.jpg
Figure 3.
Survival curves of patients by cancer type and sex in the population and sample cohorts..
epih-47-e2025058f3.jpg
Table 1.
Number of samples in the cohorts
Variables Stomach Cancer Sample Cohort
 Breast Cancer Sample Cohort
 Colorectal Cancer Sample Cohort
 Liver Cancer Sample Cohort
Population Sample cohort Population Sample cohort Population Sample cohort Population Sample cohort
Age (yr)
 0-39 6,741 1,691 (25.1) 20,264 4,178 (20.6) 7,178 1,846 (25.7) 2,418 724 (29.9)
 40-49 23,547 5,214 (22.1) 66,292 13,321 (20.1) 21,895 4,837 (22.1) 12,192 2,942 (24.1)
 50-59 55,963 11,608 (20.7) 58,075 11,714 (20.2) 59,277 12,011 (20.3) 32,870 7,026 (21.4)
 60-69 69,168 14,180 (20.5) 30,659 6,329 (20.6) 70,920 14,181 (20.0) 33,084 7,042 (21.3)
 70-79 65,297 13,358 (20.5) 13,830 3,024 (21.9) 68,748 13,729 (20.0) 30,083 6,439 (21.4)
 ≥80 27,387 5,900 (21.5) 3,787 1,020 (26.9) 33,273 6,881 (20.7) 13,677 3,202 (23.4)
Sex
 Male 167,656 34,542 (20.6) - - 158,209 31,948 (20.2) 93,053 19,821 (21.3)
 Female 80,447 17,409 (21.6) 192,907 39,586 (20.5) 103,082 21,537 (20.9) 31,271 7,554 (24.2)
Region
 Metropolitan 42,244 8,517 (20.2) 43,230 8,611 (20.2) 51,546 10,110 (19.9) 20,919 4,275 (20.4)
 City 62,976 13,741 (21.8) 50,337 10,607 (21.8) 63,939 13,383 (21.1) 32,258 7,367 (22.8)
 Rural 142,883 29,693 (20.8) 99,340 20,368 (20.8) 145,806 29,992 (20.5) 71,147 15,733 (22.2)
SEER summary stage
 In situ 10,295 2,360 (22.9) 29,304 6,035 (20.6) 29,193 6,201 (21.2) - -
 Localized 150,082 30,346 (20.2) 95,762 19,280 (20.1) 88,999 18,038 (20.3) 56,618 11,949 (21.1)
 Regional 48,703 10,440 (21.4) 54,542 11,151 (20.4) 93,026 18,848 (20.3) 29,937 6,672 (22.3)
 Distant 25,881 5,976 (23.1) 8,010 2,011 (25.1) 36,349 7,764 (21.4) 19,255 4,561 (23.7)
 Unknown 13,142 2,829 (21.5) 5,289 1,109 (21.0) 13,724 2,634 (19.2) 18,514 4,193 (22.6)
Incidence year
 2012 31,317 6,456 (20.6) 19,263 3,950 (20.5) 31,604 6,393 (20.2) 15,715 3,400 (21.6)
 2013 31,061 6,447 (20.8) 20,268 4,164 (20.5) 31,171 6,332 (20.3) 15,656 3,413 (21.8)
 2014 30,981 6,505 (21.0) 21,400 4,426 (20.7) 31,141 6,402 (20.6) 15,574 3,433 (22.0)
 2015 30,360 6,381 (21.0) 22,502 4,638 (20.6) 31,628 6,495 (20.5) 15,623 3,451 (22.1)
 2016 31,674 6,648 (21.0) 25,722 5,264 (20.5) 33,698 6,901 (20.5) 15,643 3,466 (22.2)
 2017 31,060 6,545 (21.1) 26,477 5,426 (20.5) 33,793 6,942 (20.5) 15,307 3,390 (22.1)
 2018 30,752 6,479 (21.1) 27,852 5,703 (20.5) 33,727 6,913 (20.5) 15,515 3,430 (22.1)
 2019 30,898 6,490 (21.0) 29,423 6,015 (20.4) 34,529 7,107 (20.6) 15,291 3,392 (22.2)

Values are presented as number or number (%).

SEER, Surveillance, Epidemiology and End Results.

Table 2.
Frequencies and medical costs by the type of cancer and age group
Type of cancer Age (yr) Population
 Sample cohorts
n Medical cost (USD)1 n Medical cost (USD)1
Stomach 0-39 6,741 9,398±8,320 1,691 9,604±7,955
40-49 23,547 7,869±7,588 5,214 7,893±7,400
50-59 55,963 8,194±8,401 11,608 8,188±7,609
60-69 69,168 8,562±9,849 14,180 8,588±9,604
70-79 65,297 9,397±10,511 13,358 9,270±9,941
≥80 27,387 9,623±11,504 5,900 9,672±11,711
Breast 0-39 20,264 10,380±7,716 4,178 10,391±7,725
40-49 66,292 10,074±7,800 13,321 10,112±7,801
50-59 58,075 11,297±9,291 11,714 11,341±9,381
60-69 30,659 11,501±9,156 6,329 11,431±8,577
70-79 13,830 10,999±10,368 3,024 10,997±10,521
≥80 3,787 9,487±13,238 1,020 9,288±12,448
Colorectal 0-39 7,178 9,901±10,971 1,846 10,014±11,604
40-49 21,895 10,585±11,147 4,837 10,421±10,975
50-59 59,277 10,738±11,741 12,011 10,570±11,324
60-69 70,920 11,545±12,005 14,181 11,394±11,497
70-79 68,748 12,605±13,067 13,729 12,425±12,853
≥80 33,273 12,723±14,438 6,881 12,474±13,657
Liver 0-39 2,418 14,030±13,838 724 13,765±13,926
40-49 12,192 13,581±16,928 2,942 12,923±15,101
50-59 32,870 13,558±15,303 7,026 13,316±14,218
60-69 33,084 13,327±15,902 7,042 13,218±18,131
70-79 30,083 12,372±11,945 6,439 12,421±11,611
≥80 13,677 10,197±10,829 3,202 10,133±11,667

Values are preseented as mean±standard deviation.

1 Total medical costs in the cancer incidence year; 1 US dollar (USD)=1,300 Korean won.

Table 3.
Major variables in the sample cohorts
Cohorts Domains Variables Year
Cancer registry (SMPL_RGST) Demographical factors Sex, age at cancer diagnosis 2012-2019
Cancer registry information Year and month of first diagnosis, ICD-10, ICD-O-3 (topography), ICD-O-3 (morphology), treatment (surgery, chemotherapy, radiotherapy, immunotherapy, or hormone therapy) within 4 mo after diagnosis, diagnosis method, stages 2012-2019
Cause-of-death data (SMPL_DEATH) Death Year and month of death, causes of death 2012-2021
Insurance eligibility (SMPL_BFC) Type of health insurance, residential area, deciles of insurance fee, type and grade of disability registered 2012-2022
Health insurance claim data Details of health utilization (SMPL_T200) Type of institution, region code, the type of encounter (inpatient/ outpatient), main diagnosis code, sub diagnosis code, department code, visit date, the length of stay, total days of prescription, total numbers of medication in prescription, the amount of medical costs, surgery, work-related injury, benefit extension, results of treatment code, and route through hospitalization 2012-2022
Details of treatment (SMPL_T300) Procedure codes, therapeutic material codes, medication codes in hospital, the type of treatment, amount of single dose, amount of daily dose or frequency, and total days or frequency
Details of diagnosis (SMPL_T400) Diagnosis code, type of disease code, and department code
Details of prescriptions (SMPL_T530) Prescription ID, medication code in prescription, the amount of single dose, the amount of daily dose, and total days or frequency
General health checkup Questionnaire Past medical history (stroke, cardiac infarction/angina, hypertension, diabetes, and other diseases including cancer), family history (stroke, cardiac infarction/angina, hypertension, diabetes, dyslipidemia, tuberculosis, and other diseases including cancer), smoking (including e-cigarettes), drinking, and physical activity 2012-2022
Results of examination Height, weight, body mass index, waist circumference, blood pressure, urine protein, urine glucose, urine occult blood, hemoglobin, fasting blood glucose, cholesterol (total, high-density lipoprotein, and low-density lipoprotein), triglyceride, serum creatinine, SGOT (AST), SGPT (ALT), gamma-GTP
Cancer screening Questionnaire Past cancer treatment history and family history (stomach, breast, colorectal, liver and others), experience about cancer test (UGIS or endoscopy, mammography, FOBT, colonoscopy or double contrast barium enema, pap smear, liver ultrasonography, and chest CT), and past medical history (about stomach, colorectal, liver, lung, and cervix uteri) 2012-2022
Results of examination Comprehensive evaluation result, and history of each cancer
Stomach cancer screening: UGIS or endoscopy
Breast cancer screening: mammography
Colorectal cancer screening: FOBT, colonoscopy, and double contrast barium enema
Liver cancer screening: liver ultrasonography and serum alpha-fetoprotein test

ICD-10, International Classification of Diseases-10; ICD-O-3, International Classification of Diseases for Oncology-3; AST, aspartate aminotransferase; ALT, alanine aminotransferase; SGOT, serum glutamic oxaloacetic transaminase; SGPT, serum glutamic pyruvate transaminase; GTP, glutamyl transpeptidase; UGIS, upper gastrointestinal series; FOBT, fecal occult blood test; CT, computed tomography.

Figure & Data

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      Data profile: cancer sample cohorts (stomach, breast, colorectal, and liver) in Korea
      Image Image Image
      Figure 1. Data structure of Cancer Public Library Database and four sample cohorts.
      Figure 2. Process of constructing the sample cohort data. NHIS, National Health Insurance Service; HIRA, Health Insurance Review & Assesment Service.
      Figure 3. Survival curves of patients by cancer type and sex in the population and sample cohorts..

      Figure 1.

      Figure 2.

      Figure 3.

      Data profile: cancer sample cohorts (stomach, breast, colorectal, and liver) in Korea
      Variables Stomach Cancer Sample Cohort
      		 Breast Cancer Sample Cohort
      		 Colorectal Cancer Sample Cohort
      		 Liver Cancer Sample Cohort
      Population Sample cohort Population Sample cohort Population Sample cohort Population Sample cohort
      Age (yr)
       0-39 6,741 1,691 (25.1) 20,264 4,178 (20.6) 7,178 1,846 (25.7) 2,418 724 (29.9)
       40-49 23,547 5,214 (22.1) 66,292 13,321 (20.1) 21,895 4,837 (22.1) 12,192 2,942 (24.1)
       50-59 55,963 11,608 (20.7) 58,075 11,714 (20.2) 59,277 12,011 (20.3) 32,870 7,026 (21.4)
       60-69 69,168 14,180 (20.5) 30,659 6,329 (20.6) 70,920 14,181 (20.0) 33,084 7,042 (21.3)
       70-79 65,297 13,358 (20.5) 13,830 3,024 (21.9) 68,748 13,729 (20.0) 30,083 6,439 (21.4)
       ≥80 27,387 5,900 (21.5) 3,787 1,020 (26.9) 33,273 6,881 (20.7) 13,677 3,202 (23.4)
      Sex
       Male 167,656 34,542 (20.6) - - 158,209 31,948 (20.2) 93,053 19,821 (21.3)
       Female 80,447 17,409 (21.6) 192,907 39,586 (20.5) 103,082 21,537 (20.9) 31,271 7,554 (24.2)
      Region
       Metropolitan 42,244 8,517 (20.2) 43,230 8,611 (20.2) 51,546 10,110 (19.9) 20,919 4,275 (20.4)
       City 62,976 13,741 (21.8) 50,337 10,607 (21.8) 63,939 13,383 (21.1) 32,258 7,367 (22.8)
       Rural 142,883 29,693 (20.8) 99,340 20,368 (20.8) 145,806 29,992 (20.5) 71,147 15,733 (22.2)
      SEER summary stage
       In situ 10,295 2,360 (22.9) 29,304 6,035 (20.6) 29,193 6,201 (21.2) - -
       Localized 150,082 30,346 (20.2) 95,762 19,280 (20.1) 88,999 18,038 (20.3) 56,618 11,949 (21.1)
       Regional 48,703 10,440 (21.4) 54,542 11,151 (20.4) 93,026 18,848 (20.3) 29,937 6,672 (22.3)
       Distant 25,881 5,976 (23.1) 8,010 2,011 (25.1) 36,349 7,764 (21.4) 19,255 4,561 (23.7)
       Unknown 13,142 2,829 (21.5) 5,289 1,109 (21.0) 13,724 2,634 (19.2) 18,514 4,193 (22.6)
      Incidence year
       2012 31,317 6,456 (20.6) 19,263 3,950 (20.5) 31,604 6,393 (20.2) 15,715 3,400 (21.6)
       2013 31,061 6,447 (20.8) 20,268 4,164 (20.5) 31,171 6,332 (20.3) 15,656 3,413 (21.8)
       2014 30,981 6,505 (21.0) 21,400 4,426 (20.7) 31,141 6,402 (20.6) 15,574 3,433 (22.0)
       2015 30,360 6,381 (21.0) 22,502 4,638 (20.6) 31,628 6,495 (20.5) 15,623 3,451 (22.1)
       2016 31,674 6,648 (21.0) 25,722 5,264 (20.5) 33,698 6,901 (20.5) 15,643 3,466 (22.2)
       2017 31,060 6,545 (21.1) 26,477 5,426 (20.5) 33,793 6,942 (20.5) 15,307 3,390 (22.1)
       2018 30,752 6,479 (21.1) 27,852 5,703 (20.5) 33,727 6,913 (20.5) 15,515 3,430 (22.1)
       2019 30,898 6,490 (21.0) 29,423 6,015 (20.4) 34,529 7,107 (20.6) 15,291 3,392 (22.2)
      Type of cancer Age (yr) Population
      		 Sample cohorts
      n Medical cost (USD)1 n Medical cost (USD)1
      Stomach 0-39 6,741 9,398±8,320 1,691 9,604±7,955
      40-49 23,547 7,869±7,588 5,214 7,893±7,400
      50-59 55,963 8,194±8,401 11,608 8,188±7,609
      60-69 69,168 8,562±9,849 14,180 8,588±9,604
      70-79 65,297 9,397±10,511 13,358 9,270±9,941
      ≥80 27,387 9,623±11,504 5,900 9,672±11,711
      Breast 0-39 20,264 10,380±7,716 4,178 10,391±7,725
      40-49 66,292 10,074±7,800 13,321 10,112±7,801
      50-59 58,075 11,297±9,291 11,714 11,341±9,381
      60-69 30,659 11,501±9,156 6,329 11,431±8,577
      70-79 13,830 10,999±10,368 3,024 10,997±10,521
      ≥80 3,787 9,487±13,238 1,020 9,288±12,448
      Colorectal 0-39 7,178 9,901±10,971 1,846 10,014±11,604
      40-49 21,895 10,585±11,147 4,837 10,421±10,975
      50-59 59,277 10,738±11,741 12,011 10,570±11,324
      60-69 70,920 11,545±12,005 14,181 11,394±11,497
      70-79 68,748 12,605±13,067 13,729 12,425±12,853
      ≥80 33,273 12,723±14,438 6,881 12,474±13,657
      Liver 0-39 2,418 14,030±13,838 724 13,765±13,926
      40-49 12,192 13,581±16,928 2,942 12,923±15,101
      50-59 32,870 13,558±15,303 7,026 13,316±14,218
      60-69 33,084 13,327±15,902 7,042 13,218±18,131
      70-79 30,083 12,372±11,945 6,439 12,421±11,611
      ≥80 13,677 10,197±10,829 3,202 10,133±11,667
      Cohorts Domains Variables Year
      Cancer registry (SMPL_RGST) Demographical factors Sex, age at cancer diagnosis 2012-2019
      Cancer registry information Year and month of first diagnosis, ICD-10, ICD-O-3 (topography), ICD-O-3 (morphology), treatment (surgery, chemotherapy, radiotherapy, immunotherapy, or hormone therapy) within 4 mo after diagnosis, diagnosis method, stages 2012-2019
      Cause-of-death data (SMPL_DEATH) Death Year and month of death, causes of death 2012-2021
      Insurance eligibility (SMPL_BFC) Type of health insurance, residential area, deciles of insurance fee, type and grade of disability registered 2012-2022
      Health insurance claim data Details of health utilization (SMPL_T200) Type of institution, region code, the type of encounter (inpatient/ outpatient), main diagnosis code, sub diagnosis code, department code, visit date, the length of stay, total days of prescription, total numbers of medication in prescription, the amount of medical costs, surgery, work-related injury, benefit extension, results of treatment code, and route through hospitalization 2012-2022
      Details of treatment (SMPL_T300) Procedure codes, therapeutic material codes, medication codes in hospital, the type of treatment, amount of single dose, amount of daily dose or frequency, and total days or frequency
      Details of diagnosis (SMPL_T400) Diagnosis code, type of disease code, and department code
      Details of prescriptions (SMPL_T530) Prescription ID, medication code in prescription, the amount of single dose, the amount of daily dose, and total days or frequency
      General health checkup Questionnaire Past medical history (stroke, cardiac infarction/angina, hypertension, diabetes, and other diseases including cancer), family history (stroke, cardiac infarction/angina, hypertension, diabetes, dyslipidemia, tuberculosis, and other diseases including cancer), smoking (including e-cigarettes), drinking, and physical activity 2012-2022
      Results of examination Height, weight, body mass index, waist circumference, blood pressure, urine protein, urine glucose, urine occult blood, hemoglobin, fasting blood glucose, cholesterol (total, high-density lipoprotein, and low-density lipoprotein), triglyceride, serum creatinine, SGOT (AST), SGPT (ALT), gamma-GTP
      Cancer screening Questionnaire Past cancer treatment history and family history (stomach, breast, colorectal, liver and others), experience about cancer test (UGIS or endoscopy, mammography, FOBT, colonoscopy or double contrast barium enema, pap smear, liver ultrasonography, and chest CT), and past medical history (about stomach, colorectal, liver, lung, and cervix uteri) 2012-2022
      Results of examination Comprehensive evaluation result, and history of each cancer
      Stomach cancer screening: UGIS or endoscopy
      Breast cancer screening: mammography
      Colorectal cancer screening: FOBT, colonoscopy, and double contrast barium enema
      Liver cancer screening: liver ultrasonography and serum alpha-fetoprotein test
      Table 1. Number of samples in the cohorts

      Values are presented as number or number (%).

      SEER, Surveillance, Epidemiology and End Results.

      Table 2. Frequencies and medical costs by the type of cancer and age group

      Values are preseented as mean±standard deviation.

      Total medical costs in the cancer incidence year; 1 US dollar (USD)=1,300 Korean won.

      Table 3. Major variables in the sample cohorts

      ICD-10, International Classification of Diseases-10; ICD-O-3, International Classification of Diseases for Oncology-3; AST, aspartate aminotransferase; ALT, alanine aminotransferase; SGOT, serum glutamic oxaloacetic transaminase; SGPT, serum glutamic pyruvate transaminase; GTP, glutamyl transpeptidase; UGIS, upper gastrointestinal series; FOBT, fecal occult blood test; CT, computed tomography.

      Table 1.

      Table 2.

      Table 3.

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